1. Field of the Invention
The present invention relates to an image processing apparatus and method for processing image data including a fundamental stimulus value and spectral auxiliary coefficient.
2. Description of the Related Art
The colors of printing materials for forming an output image by a conventional color printing apparatus are generally three, cyan (C), magenta (M), and yellow (Y) subtractive primary colors, or four colors including black (K) in addition to these three colors. In this case, three, red (R), green (G), and blue (B) color components of input image data are converted into three, C, M, and Y colors or four, C, M, Y, and K colors, forming an image with printing materials of the respective colors. These days, color printing apparatuses using basic colorant materials of four, C, M, Y, and K colors, and those of three, R, G, and B spot colors other than the three subtractive primary colors have appeared on the market. This color printing apparatus can implement color reproduction which has not been achieved by conventional three- or four-color image formation.
Along with recent rapid popularization of color printing apparatuses, a demand for higher image quality is growing. There is proposed the use of spectral information of the visible wavelength region as information input to a color printing apparatus. Recently, a multi-band camera with five or six sensitivities is used as a device for acquiring spectral information of an image. The multi-band camera can acquire the spectral reflectance factor of an object that cannot be obtained by a conventional 3-channel camera. The multi-band camera and the color printing apparatus using spot color inks can be combined into a color reproduction system capable of reproducing spectral information of an object.
In an image processing (to be referred to as a spectral image processing hereinafter) for processing spectral information, the output colors of the color printing apparatus need to be determined to minimize a spectral error from spectral information input from the multi-band camera. The spectral image processing can provide the perceived color on an output image regardless of the observation environment such as the environment light source. That is, the spectral image processing can reduce metamerism.
However, the spectral image processing greatly increases the number of dimensions of process data in comparison with tristimulus values such as CIELAB or CIEXYZ. For example, when spectral information is sampled at intervals of 10 nm from 400 nm to 700 nm, the number of dimensions of obtained spectral data becomes 31. To execute a simpler spectral image processing, it is important to reduce the number of dimensions and effectively compress data without impairing spectral characteristics.
As a spectral information data compression method, principal component analysis is done for input spectral image data to hold weighting factor data for each principal component (see, e.g., Japanese Patent Laid-Open No. 2005-78171). According to this proposal, spectral intensity data of a thinned-out or downscaled image is used together with sRGB fundamental color data, so image data can be processed as one having conventional RGB data.
As another spectral information data compression method, a spectral information compression method using a six-dimensional spectral color space LabPQR is proposed (see, e.g., M. Derhak, M. Rosen, “Spectral Colorimetry Using LabPQR—An Interim Connection Space”, “Color Imaging Conference 2004”, USA, Imaging Science and Technology, November 2004, pp. 246-250). Since LabPQR includes L*a*b* (L*, a*, and b*) values, color reproduction identical to colorimetric color reproduction can be achieved under a specific L*a*b* value-dependent observation condition. Further, since LabPQR includes spectral information PQR, metamerism can be reduced.
However, according to the method proposed in Japanese Patent Laid-Open No. 2005-78171, spectral intensity data is expressed by the weighting factor of principal component analysis of input spectral image data. Information redundancy remains between fundamental color data and spectral intensity data. When the number of dimensions of data necessary to hold spectral image information is six, the number of dimensions of data compression becomes nine including three-dimensional fundamental color data, increasing the capacity of data processed when performing an image processing.
According to the method proposed in “Spectral Colorimetry Using LabPQR—An Interim Connection Space”, P, Q, and R (PQR) images serving as spectral information undergo the same calculation process when compressing input spectral information via the 6D spectral color space LabPQR. The method proposed in this reference requires a large memory capacity to save LabPQR images.